Marabou 2.0: A Versatile Formal Analyzer of Neural Networks

1 code implementation • 25 Jan 2024 • Haoze Wu, Omri Isac, Aleksandar Zeljić, Teruhiro Tagomori, Matthew Daggitt, Wen Kokke, Idan Refaeli, Guy Amir, Kyle Julian, Shahaf Bassan, Pei Huang, Ori Lahav, Min Wu, Min Zhang, Ekaterina Komendantskaya, Guy Katz, Clark Barrett

This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.

Efficient Neural Network Analysis with Sum-of-Infeasibilities

2 code implementations • 19 Mar 2022 • Haoze Wu, Aleksandar Zeljić, Guy Katz, Clark Barrett

Given a convex relaxation which over-approximates the non-convex activation functions, we encode the violations of activation functions as a cost function and optimize it with respect to the convex relaxation.

Adversarial Attack Efficient Neural Network

Global Optimization of Objective Functions Represented by ReLU Networks

no code implementations • 7 Oct 2020 • Christopher A. Strong, Haoze Wu, Aleksandar Zeljić, Kyle D. Julian, Guy Katz, Clark Barrett, Mykel J. Kochenderfer

However, individual "yes or no" questions cannot answer qualitative questions such as "what is the largest error within these bounds"; the answers to these lie in the domain of optimization.

Parallelization Techniques for Verifying Neural Networks

no code implementations • 17 Apr 2020 • Haoze Wu, Alex Ozdemir, Aleksandar Zeljić, Ahmed Irfan, Kyle Julian, Divya Gopinath, Sadjad Fouladi, Guy Katz, Corina Pasareanu, Clark Barrett

Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification.